Search Results for author:
Found 10 papers, 8 papers with code
AutoMind: Adaptive Knowledgeable Agent for Automated Data Science
Large Language Model (LLM) agents have shown great potential in addressing real-world data science problems.
How Do LLMs Acquire New Knowledge? A Knowledge Circuits Perspective on Continual Pre-Training
Despite exceptional capabilities in knowledge-intensive tasks, Large Language Models (LLMs) face a critical gap in understanding how they internalize new knowledge, particularly how to structurally embed acquired knowledge in their neural computations.
Symbolic Learning Enables Self-Evolving Agents
In this work, we introduce agent symbolic learning, a systematic framework that enables language agents to optimize themselves on their own in a data-centric way using symbolic optimizers.
KnowAgent: Knowledge-Augmented Planning for LLM-Based Agents
Large Language Models (LLMs) have demonstrated great potential in complex reasoning tasks, yet they fall short when tackling more sophisticated challenges, especially when interacting with environments through generating executable actions.
EasyInstruct: An Easy-to-use Instruction Processing Framework for Large Language Models
In recent years, instruction tuning has gained increasing attention and emerged as a crucial technique to enhance the capabilities of Large Language Models (LLMs).
OceanGPT: A Large Language Model for Ocean Science Tasks
Ocean science, which delves into the oceans that are reservoirs of life and biodiversity, is of great significance given that oceans cover over 70% of our planet's surface.
Layout and Task Aware Instruction Prompt for Zero-shot Document Image Question Answering
Instead, in this paper, we find that instruction-tuning language models like Claude and ChatGPT can understand layout by spaces and line breaks.
Ranked #9 on
Visual Question Answering (VQA)
on DocVQA test
LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities
We engage in experiments across eight diverse datasets, focusing on four representative tasks encompassing entity and relation extraction, event extraction, link prediction, and question-answering, thereby thoroughly exploring LLMs' performance in the domain of construction and inference.
A Concept Knowledge Graph for User Next Intent Prediction at Alipay
This paper illustrates the technologies of user next intent prediction with a concept knowledge graph.
Reasoning with Language Model Prompting: A Survey
Reasoning, as an essential ability for complex problem-solving, can provide back-end support for various real-world applications, such as medical diagnosis, negotiation, etc.
